Clustering mining method of large-scale network abnormal data based on selective collaborative learning

Abstract

In order to improve the accuracy and recall rate of the clustering mining process of large-scale network abnormal data and shorten the time of clustering mining, in this study, a large-scale network anomaly data clustering mining method based on selective collaborative learning is proposed. Through cooperative training and selective ensemble learning, a machine learning anomaly detection model and a strong classifier for large-scale network data are designed, and the correlation variable analysis method is used to obtain the dissimilarity measure of data. The network anomaly data is processed by fuzzy fusion, and the nearest neighbor algorithm is used to realize the clustering mining of large scale network anomaly data. The data clustering mining accuracy of this method reaches 98.16%, the time of data clustering mining is only 2.5 s, and the recall rate of data clustering mining is up to 98.38%, indicating that this method can improve the effect of large-scale network anomaly data clustering mining.